Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Modified Steering Kernel Filter for AWGN Removal based on Kernel Similarity

  • Cheon, Bong-Won (Department of Intelligent Robot Engineering, Pukyong National University) ;
  • Kim, Nam-Ho (School of Electrical Engineering, Pukyong National University)
  • Received : 2021.12.24
  • Accepted : 2022.08.24
  • Published : 2022.09.30
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Abstract

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

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References

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